Biology subjects

Liao, K.

Publications and source records attributed to Liao, K..

13 recordsLinked to original sources

bioRxiv2026

p75 neurotrophin receptor signaling through the RhoA/ROCK pathway contributes to Alzheimers Disease tauopathy

Therapeutic development in Alzheimers Disease (AD) has for the most part been focused on reducing {beta}-amyloid load. Nevertheless, neurofibrillary tangles (NFTs), produced by aggregation of hyper-phosphorylated Tau protein, correlate with neurodegeneration and cognitive impairment significantly better than amyloid accumulation in AD patients. Here we report that P301S mice, a model of AD tauopathy, carrying mutant variants of the p75 neurotrophin receptor (p75NTR) deficient in RhoA/ROCK signaling are protected from neurodegeneration and cognitive impairment. Both p75{Delta}DD, lacking the death domain, and triple mutant p75KKEA, unable to interact with RhoGDI, decreased NFT levels, reduced gliosis, neurodegeneration and synapse loss, and improved spatial learning and memory in P301S mice. Intriguingly, p75C259A, a variant unresponsive to neurotrophins but still competent for RhoA signaling induced by myelin-derived ligands, did not afford any neuroprotection. P301S neurons expressing p75{Delta}DD or p75KKEA, but not p75C259A, showed reduced phospho-Tau and ROCK and GSK3{beta} activity, the two main kinases responsible for Tau phosphorylation. In line with this, treatment with myelin-associated glycoprotein (MAG) enhanced Tau phosphorylation and ROCK activity in P301S neurons expressing wild type p75NTR or p75C259A, but not p75{Delta}DD or p75KKEA. Together, these results indicate that p75NTR contributes to AD tauopathy by enhancing the activity of the RhoA-ROCK pathway.

neuroscience↗

bioRxiv2025

Semiparametric Confidence Sets for Arbitrary Effect Sizes in Longitudinal Neuroimaging

The majority of neuroimaging inference focuses on hypothesis testing rather than effect estimation. With concerns about replicability, there is growing interest in reporting standardized effect sizes from neuroimaging group-level analyses. Confidence sets are a recently developed approach to perform inference for effect sizes in neuroimaging but are restricted to univariate effect sizes and cross-sectional data. Thus, existing methods exclude increasingly common multigroup or nonlinear longitudinal associations of biological brain measurements with inter- and intra-individual variations in diagnosis, development, or symptoms. We broadly generalize the confidence set approach by developing a method for arbitrary effect sizes in longitudinal studies. Our method involves robust estimation of the effect size image and spatial and temporal covariance function based on generalized estimating equations. We obtain more efficient effect size estimates by concurrently estimating the exchangeable working covariance and using a nonparametric bootstrap to determine the joint distribution of effect size across voxels used to construct confidence sets. These confidence sets identify regions of the image where the lower or upper simultaneous confidence interval is above or below a given threshold with high probability. We evaluate the coverage and simultaneous confidence interval width of the proposed procedures using realistic simulations and perform longitudinal analyses of aging and diagnostic differences of cortical thickness in Alzheimers disease and diagnostic differences of resting-state hippocampal activity in psychosis. This comprehensive approach along with the visualization functions integrated into the pbj R package offers a robust tool for analyzing repeated neuroimaging measurements.

neuroscience↗

bioRxiv2024

Genomic evolution reshapes cell type diversification in the amniote brain

Over 320 million years of evolution, amniotes have developed complex brains and cognition through largely unexplored genetic and gene expression mechanisms. We created a comprehensive single-cell atlas of over 1.3 million cells from the telencephalon and cerebellum of turtles, zebra finches, pigeons, mice, and macaques, employing single-cell resolution spatial transcriptomics to validate gene expression patterns across species. Our study revealed significant species-specific variations in cell types, highlighting their conservation and diversification in evolution. We found pronounced differences in telencephalon excitatory neurons (EX) and cerebellar cell types between birds and mammals. Birds predominantly express SLC17A6 in EX, whereas mammals expressed SLC17A7 in neocortex and SLC17A6 elsewhere, possibly due to loss of SLC17A7 function loss in birds. Additionally, we identified a novel bird-specific Purkinje cell subtype (SVIL+), implicating the LSD11/KDM1A pathway in learning and circadian rhythms, and related numerous positively selected genes in birds, suggesting an evolutionary optimization of cerebellar functions for ecological and behavioral adaptation. Our findings elucidate the complex interplay between genetic evolution and environmental adaptation, underscoring the role of genetic diversification in the development of specialized cell types across amniotes.

evolutionary biology↗

bioRxiv2024

Augmentation of DNA exonuclease TREX1 in macrophages as a therapy for cardiac ischemic injury

Noncoding RNAs (ncRNAs) are increasingly recognized as bioactive. Here we report the development of TY1, a synthetic ncRNA bioinspired by a naturally-occurring human small Y RNA with immunomodulatory properties. TY1 upregulates TREX1, an exonuclease that rapidly degrades cytosolic DNA. In preclinical models of myocardial infarction (MI) induced by ischemia/reperfusion, TY1 reduced scar size. The cardioprotective effect of TY1 was abrogated by prior depletion of macrophages and mimicked by adoptive transfer of macrophages exposed either to TY1 or TREX1. Inhibition of TREX1 in macrophages blocked TY1 cardioprotection. Consistent with a central role for TREX1, TY1 attenuated DNA damage in the post-MI heart. This novel mechanism--pharmacologic upregulation of TREX1 in macrophages--establishes TY1 as the prototype for a new class of ncRNA drugs with disease-modifying bioactivity. One Sentence SummaryUpregulation of three prime exonuclease, TREX1, in macrophages enhances tissue repair post myocardial infarction.

molecular biology↗

bioRxiv2024

Age-related RPE Changes in Wildtype C57BL/6J Mice Between 2 and 32 Months

PurposeThis study provides a systematic evaluation of age-related changes in RPE cell structure and function using a morphometric approach. We aim to better capture nuanced predictive changes in cell heterogeneity that reflect loss of RPE integrity during normal aging. Using C57BL6/J mice ranging from P60-P730, we sought to evaluate how regional changes in RPE shape reflect incremental losses in RPE cell function with advancing age. We hypothesize that tracking global morphological changes in RPE is predictive of functional defects over time. MethodsWe tested three groups of C57BL/6J mice (young: P60-180; Middle-aged: P365-729; aged: 730+) for function and structural defects using electroretinograms, immunofluorescence, and phagocytosis assays. ResultsThe largest changes in RPE morphology were evident between the young and aged groups, while the middle-aged group exhibited smaller but notable region-specific differences. We observed a 1.9-fold increase in cytoplasmic alpha-catenin expression specifically in the central-medial region of the eye between the young and aged group. There was an 8-fold increase in subretinal, IBA-1-positive immune cell recruitment and a significant decrease in visual function in aged mice compared to young mice. Functional defects in the RPE corroborated by changes in RPE phagocytotic capacity. ConclusionsThe marked increase of cytoplasmic alpha-catenin expression and subretinal immune cell deposition, and decreased visual output coincide with regional changes in RPE cell morphometrics when stratified by age. These cumulative changes in the RPE morphology showed predictive regional patterns of stress associated with loss of RPE integrity.

cell biology↗

bioRxiv2023

A single-cell transcriptome analysis and prognostic model construction of stromal cells for renal cell carcinoma

Renal cell carcinoma (RCC) is among the top three cancers of the urinary system and its incidence keeps increasing worldwide in recent decades. However, methods for accurate prognosis evaluation and effective treatment are still lacking nowadays. Here, to explore the molecular expression features of RCC and establish a new RCC clinical prognosis evaluation model, a cell landscape of 187,263 renal cells obtained from eight patients with RCC was analyzed in this study. And by extracting and focusing on the main stromal cells from RCC tissues, innovative molecular characteristics and pathways of tumors were identified, like the well-known hypoxia pathway. By analyzing cell-cell communication, fibroblasts were found to promote tumor development by repressing natural killer cells. Based on Cox and least absolute shrinkage and selection operator regression analysis, four risk factors were screened and used to construct a reliable RCC clinical risk estimation model. In conclusion, our work provides new insights into the tumor microenvironment of RCC, as well as potential therapeutic targets and a clinical risk model for RCC invasiveness. Hopefully, these findings will be useful for cancer research and clinical treatment in future.

cancer biology↗

bioRxiv2023

Contextualizing protein representations using deep learning on protein networks and single-cell data

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce PO_SCPLOWINNACLEC_SCPLOW, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multi-organ single-cell atlas, PO_SCPLOWINNACLEC_SCPLOW learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. PO_SCPLOWINNACLEC_SCPLOWs embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs effects across cell types. PO_SCPLOWINNACLEC_SCPLOW outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and pinpoints cell type contexts with higher predictive capability than context-free models. PO_SCPLOWINNACLEC_SCPLOWs ability to adjust its outputs based on the context in which it operates paves way for large-scale context-specific predictions in biology.

bioinformatics↗

bioRxiv2020

ATAC-seq with unique molecular identifiers improves quantification and footprinting

ATAC-seq (Assay for Transposase-Accessible Chromatin with high-throughput sequencing) provides an efficient way to analyze nucleosome-free regions and has been applied widely to identify transcription factor footprints. Both applications rely on the accurate quantification of insertion events of the hyperactive transposase Tn5. However, due to the presence of the PCR amplification, it is impossible to accurately distinguish independently generated identical Tn5 insertion events from PCR duplicates using the standard ATAC-seq technique. Removing PCR duplicates based on mapping coordinates introduces an increasing bias towards highly accessible chromatin regions. To overcome this limitation, we establish a UMI-ATAC-seq technique by incorporating unique molecular identifiers (UMIs) into standard ATAC-seq procedures. In our study, UMI-ATAC-seq can rescue about 20% of reads that are mistaken as PCR duplicates in standard ATAC-seq, which helps identify an additional 50% or more of footprints. We demonstrate that UMI-ATAC-seq could more accurately quantify chromatin accessibility and significantly improve the sensitivity of identifying transcription factor footprints. An analytic pipeline is developed to facilitate the application of UMI-ATAC-seq, and it is available at https://github.com/tzhu-bio/UMI-ATAC-seq.

genomics↗

bioRxiv2020

Dynamics of mcr-1 prevalence and mcr-1-positive Escherichia coli after the cessation of colistin use as a feed additive for animals in China: a prospective cross-sectional and whole genome sequencing based molecular epidemiological study

BackgroundThe global dissemination of colistin resistance encoded by mcr-1 has been attributed to extensive use of colistin in livestock, threatening colistin efficacy in medicine. The emergence of mcr-1 in common pathogens, such as Escherichia coli, is of particular concern. Therefore, China banned the use of colistin in animal feed from May 1ST 2017. We investigated subsequent changes in mcr-1 prevalence, and the genomic epidemiology of mcr-1-positive Escherichia coli (MCRPEC). MethodsSampling was conducted pre- (October-December 2016) and post-colistin ban (October-December, 2017 and 2018, respectively). 3675 non-duplicate pig fecal samples were collected from 14 provinces (66 farms) in China to determine intervention-related changes in mcr-1 prevalence. 15193 samples were collected from pigs, healthy human volunteers, colonized and infected hospital inpatients, food and the environment in Guangzhou, to characterize source-specific mcr-1 prevalence and the wider ecological impact of the ban. From these samples, 688 MCRPEC were analyzed with whole genome sequencing (WGS), plasmid conjugation and S1-PFGE/Southern blots to characterize associated genomic changes. FindingsAfter the ban, mcr-1 prevalence decreased significantly in national pig farms, from 45{middle dot}0% (308/684 samples) in 2016, to 19{middle dot}4% (274/1416) in 2018 (p<0{middle dot}0001). This trend was mirrored in samples from most sources in Guangzhou (overall 19{middle dot}2% [959/5003 samples] in 2016; 5{middle dot}3% [238/4489] in 2018; p<0{middle dot}0001). The population structure of MCRPEC was diverse (23 sequence clusters [SCs]); ST10 clonal complex isolates were predominant (247/688 [36%]). MCRPEC causing infection in hospitalized inpatients were genetically more distinct and appeared less affected by the ban. mcr-1 was predominantly found on plasmids (632/688 [92%]). Common mcr-1 plasmid types included IncX4, IncI2 and IncHI2 (502/656 [76.5%]); significant increases in IncI2-associated mcr-1 and a distinct lineage of mcr-1-associated IncHI2 were observed post-ban. Changes in the frequency of mcr-1-associated flanking sequences (ISApl1-negative MCRPEC), 63 core genome SNPs and 30 accessory genes were also significantly different after the ban, consistent with rapid genetic adaptation in response to changing selection pressures. InterpretationA rapid, ecosystem-wide, decline in mcr-1 was observed after banning the use of colistin in animal feed, with associated genetic changes in MCRPEC. Genomic surveillance is key to assessing and monitoring stewardship interventions. FundingNational Natural Science Foundation of China

microbiology↗

bioRxiv2019

Using genetics to differentiate patients with similar symptoms: application to inflammatory arthritis in the rheumatology outpatient clinic

Slow developing complex diseases are a clinical diagnostic challenge. Since genetic information is increasingly available prior to a patients first visit to a clinic, it might improve diagnostic accuracy. We aimed to devise a method to convert genetic information into simple probabilities discriminating between multiple diagnoses in patients presenting with inflammatory arthritis.\n\nWe developed G-Prob, which calculates for each patient the genetic probability for each of multiple possible diseases. We tested this for inflammatory arthritis-causing diseases (rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis and gout). After validating in simulated data, we tested G-Prob in biobank cohorts in which genetic data were linked to electronic medical records: - 1,200 patients identified by ICD-codes within the eMERGE database (n= 52,623);\n- 245 patients identified through ICD codes and review of medical records within the Partners Biobank (n=12,604);\n- 243 patients selected prospectively with final diagnoses by medical record review within the Partners Biobank (n=12,604).\nThe calibration of G-Prob with the disease status was high (with regression coefficients ranging from 0.90-1.08 (ideal would be 1.00) in all cohorts. G-Probs discriminative ability was high in all cohorts with pooled Area Under the Curve (AUC)=0.69 [95%CI 0.67-0.71], 0.81 [95%CI 0.76-0.84] and 0.84 [95%CI 0.81-0.86]. For all patients, at least one disease could be ruled out, and in 45% of patients a most likely diagnosis could be identified with an overall 64% positive predictive value. In 35% of instances the clinicians initial diagnosis was incorrect. Initial clinical diagnosis explained 39% of the variance in final disease prediction which improved to 51% (P<0.0001) by adding G-Prob genetic data.\n\nIn conclusion, by converting genotypes into an interpretable probability value for five different inflammatory arthritides, we can better discriminate and diagnose rheumatic diseases. Genotypes available prior to a clinical visit could be considered part of patients medical history and potentially used to improve precision and diagnostic efficiency in clinical practice.

genetics↗

bioRxiv2019

Expressing remorin in animal cells forms filamentous structure unraveling the mechanism of plasmodesmata biogenesis

Plasmodesmata (PD) are crucial structures for intercellular communication in multicellular plants with remorins being their crucial plant-specific structural and functional constituents. The PD biogenesis is an intriguing but poorly understood process. By expressing an Arabidopsis remorin protein in mammalian cells, we have reconstituted a PD-like filamentous structure, termed remorin filament (RF), connecting neighboring cells physically and physiologically. Notably, RFs are capable of transporting macromolecules intercellularly, in a way similar to plant PD. With further super-resolution microscopic analysis and biochemical characterization, we found that RFs are also composed of actin filaments, forming the core skeleton structure, aligned with the remorin protein. This unique heterologous filamentous structure might explain the molecular mechanism for remorin function as well as PD construction. Furthermore, remorin protein exhibits a specific distribution manner in the plasma membrane in mammalian cells, representing a lipid nanodomain, depending on its lipid modification status. Our studies not only provide crucial insights into the mechanism of PD biogenesis, but also uncovers unsuspected fundamental mechanistic and evolutionary links between intercellular communication systems of plants and animals. SignificanceRemorin is rising as a crucial lipid microdomain marker, as well as an essential component of plasmodesmata in plants. However, the biological role of remorin in plants is elusive. With a heterologous system, we found that remorin expression is able to form intercellular filamentous structure, namely remorin filament (RF), connecting neighboring mammalian cells functionally. By employing multiple approaches, we tested the functionality of RFs, as well as investigated their structures. RFs highly resemble plant plasmodesmata, in many ways, such as its morphology, molecular constitutes, and its ability to transport macromolecules intercellularly. Our study provides novel insights into the biogenesis of plasmodesmata and uncovers fundamental evolutionary links in molecular construction of intercellular connections in both plants and animals.

cell biology↗

bioRxiv2019

Different Expressions of IL-1 TNF-α P-selectin mRNA by Endothelial Cells after Vein Thrombosis

[Abstract]O_ST_ABSObjectiveC_ST_ABSExperiments were designed to compare the expressions of IL-1 TNF- P-selectin mRNA by porcine endothelial cells after vein thrombosis.\n\nMethodsIVC under the renal vein of 20 porcines were ligated to induce thrombosis modes. These thrombosed veins were divided into three groups:group A-one day after thrombosis, group B-four days after thrombosis and group C-seven days after thrombosis. The other pigs were given the shame operation as a contro group (group D). The mRNA levels of IL-1 TNF- and P-selectin expressed by porcine endothelial cells in three groups were analy sed by semi quantitative RT-PCR. Endothelial cells were harvested with collagenase II.\n\nResultsThe purity of endothelial cells harvested was 99.42 {+/-}0.07. The expression of IL-1 was detained only in group A while TNF-reached its peak in group B(P <0.05) and P-selectin increased gradually with the days passing by(P <0.05).\n\nConclusionEndothelial cells are not only the target cells of inflammatory mediators, but also can express a variety of active factors to promote venous thrombosis. Expression of TNF- mRNA is increased gradually in the early period of vein thrombosis while P-selectin in the acute period; IL-1 mRNA was transiently expressed only in the early stage of thrombosis.

microbiology↗

bioRxiv2017

Identification of Cancer-associated Metabolic Vulnerabilities by Modeling Multi-objective Optimality in Metabolism

Computational modeling of the genome-wide metabolic network is essential for designing new therapeutics targeting cancer-associated metabolic disorder, which is a hallmark of human malignancies. However, previous studies generally assumed that metabolic fluxes of cancer cells are subjected to the maximization of biomass production, despite the wide existence of trade-offs among multiple metabolic objectives. To address this issue, we developed a multi-objective model of cancer metabolism with algorithms depicting approximate Pareto surfaces and incorporating multiple omics datasets. To validate this approach, we built individualized models for NCI-60 cancer cell lines, and accurately predicted cell growth rates and other biological consequences of metabolic perturbations in these cells. By analyzing the landscape of approximate Pareto surface, we identified a list of metabolic targets essential for cancer cell proliferation and the Warburg effect, and further demonstrated their close association with cancer patient survival. Finally, metabolic targets predicted to be essential for tumor progression were validated by cell-based experiments, confirming this multi-objective modelling method as a novel and effective strategy to identify cancer-associated metabolic vulnerabilities.

systems biology↗